Choosing unlabeled examples for SemiBoost using modified cuckoo search algorithms

SemiBoost (SB) is a boosting form, in which both labeled and unlabeled examples are contributed to learning. In order to perform the task of choosing unlabeled examples in SB, various strategies have been proposed in the literature. Meanwhile, cuckoo search (CS) is a novel meta-heuristic algorithm, whose obtained solutions are better than solutions obtained by efficient particle swarm optimizer (PSO) and genetic algorithm (GA). Recently, rather than using the original CS algorithm, some kinds of modifications have been proposed to improve its searching performance. This paper presents an empirical comparison, using modified CS algorithms to select useful unlabeled examples for SB learning. In particular, several modification strategies were considered and compared empirically, including a CS modified by using the probability estimate in initialization (CS-pE); a modification for unconstrained optimization problems by changing the step-size; a modified CS using Centroidal Voronoi Tessellations in initialization; and modified CSs using adaptive parameters. The experimental results show that the CS-pE strategy works better than the other modifications in synthetic and real-life benchmark data in terms of classification accuracy. In addition, the results show that when a set of the fine tuned parameters is provided and the searching process is appropriately initialized, the strategy achieves further improvement in the accuracy.